{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mushroom Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, I will make use of the Mushroom Classification dataset to try to predict if a Mushroom is poisonous or not by looking at the given features. I will successivelly try differerent feature elimination techniques to see how this can affect training times and overall model accuracy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reducing the number of features in a dataset, can lead to:\n",
    "- Accuracy improvements\n",
    "- Overfitting risk reduction\n",
    "- Speed up in training\n",
    "- Improved Data Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/kaggle/input/mushroom-classification/mushrooms.csv\n"
     ]
    }
   ],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from matplotlib.pyplot import figure\n",
    "from sklearn.utils import shuffle\n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import time\n",
    "import os\n",
    "\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>cap-shape</th>\n",
       "      <th>cap-surface</th>\n",
       "      <th>cap-color</th>\n",
       "      <th>bruises</th>\n",
       "      <th>odor</th>\n",
       "      <th>gill-attachment</th>\n",
       "      <th>gill-spacing</th>\n",
       "      <th>gill-size</th>\n",
       "      <th>gill-color</th>\n",
       "      <th>stalk-shape</th>\n",
       "      <th>stalk-root</th>\n",
       "      <th>stalk-surface-above-ring</th>\n",
       "      <th>stalk-surface-below-ring</th>\n",
       "      <th>stalk-color-above-ring</th>\n",
       "      <th>stalk-color-below-ring</th>\n",
       "      <th>veil-type</th>\n",
       "      <th>veil-color</th>\n",
       "      <th>ring-number</th>\n",
       "      <th>ring-type</th>\n",
       "      <th>spore-print-color</th>\n",
       "      <th>population</th>\n",
       "      <th>habitat</th>\n",
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       "      <td>2</td>\n",
       "      <td>e</td>\n",
       "      <td>b</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>t</td>\n",
       "      <td>l</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>n</td>\n",
       "      <td>e</td>\n",
       "      <td>c</td>\n",
       "      <td>s</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>p</td>\n",
       "      <td>x</td>\n",
       "      <td>y</td>\n",
       "      <td>w</td>\n",
       "      <td>t</td>\n",
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       "      <td>4</td>\n",
       "      <td>e</td>\n",
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       "      <td>a</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "  class cap-shape cap-surface cap-color bruises odor gill-attachment  \\\n",
       "0     p         x           s         n       t    p               f   \n",
       "1     e         x           s         y       t    a               f   \n",
       "2     e         b           s         w       t    l               f   \n",
       "3     p         x           y         w       t    p               f   \n",
       "4     e         x           s         g       f    n               f   \n",
       "\n",
       "  gill-spacing gill-size gill-color stalk-shape stalk-root  \\\n",
       "0            c         n          k           e          e   \n",
       "1            c         b          k           e          c   \n",
       "2            c         b          n           e          c   \n",
       "3            c         n          n           e          e   \n",
       "4            w         b          k           t          e   \n",
       "\n",
       "  stalk-surface-above-ring stalk-surface-below-ring stalk-color-above-ring  \\\n",
       "0                        s                        s                      w   \n",
       "1                        s                        s                      w   \n",
       "2                        s                        s                      w   \n",
       "3                        s                        s                      w   \n",
       "4                        s                        s                      w   \n",
       "\n",
       "  stalk-color-below-ring veil-type veil-color ring-number ring-type  \\\n",
       "0                      w         p          w           o         p   \n",
       "1                      w         p          w           o         p   \n",
       "2                      w         p          w           o         p   \n",
       "3                      w         p          w           o         p   \n",
       "4                      w         p          w           o         e   \n",
       "\n",
       "  spore-print-color population habitat  \n",
       "0                 k          s       u  \n",
       "1                 n          n       g  \n",
       "2                 n          n       m  \n",
       "3                 k          s       u  \n",
       "4                 n          a       g  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('/kaggle/input/mushroom-classification/mushrooms.csv')\n",
    "pd.options.display.max_columns = None\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>percent_missing</th>\n",
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       "    <tr>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>ring-number</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <td>veil-color</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <td>veil-type</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>stalk-color-below-ring</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>stalk-shape</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <td>gill-color</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>gill-size</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>gill-spacing</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>gill-attachment</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>odor</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>bruises</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cap-color</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>cap-surface</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>habitat</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          percent_missing\n",
       "class                                 0.0\n",
       "stalk-surface-above-ring              0.0\n",
       "population                            0.0\n",
       "spore-print-color                     0.0\n",
       "ring-type                             0.0\n",
       "ring-number                           0.0\n",
       "veil-color                            0.0\n",
       "veil-type                             0.0\n",
       "stalk-color-below-ring                0.0\n",
       "stalk-color-above-ring                0.0\n",
       "stalk-surface-below-ring              0.0\n",
       "stalk-root                            0.0\n",
       "cap-shape                             0.0\n",
       "stalk-shape                           0.0\n",
       "gill-color                            0.0\n",
       "gill-size                             0.0\n",
       "gill-spacing                          0.0\n",
       "gill-attachment                       0.0\n",
       "odor                                  0.0\n",
       "bruises                               0.0\n",
       "cap-color                             0.0\n",
       "cap-surface                           0.0\n",
       "habitat                               0.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percent_missing = df.isnull().sum() * 100 / len(df)\n",
    "missing_values = pd.DataFrame({'percent_missing': percent_missing})\n",
    "missing_values.sort_values(by ='percent_missing' , ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"ticks\")\n",
    "f = sns.countplot(x=\"class\", data=df, palette=\"bwr\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "e    4208\n",
       "p    3916\n",
       "Name: class, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['class'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8124, 23)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now divide our dataset in features (X) and labels (Y). We then transoform all our dataset categorical features in numeric features using One Hot Encoding. <br> <br>\n",
    "A new column gets created for all the diffent cases in a categorical feature. <br> <br>\n",
    "For example, the **bruises** feature contains two categorical cases **f** and **t**. This categorical feature will be splitted in two numeric features, one having 1s in all the rows for which the mushroom which had bruises f (**bruises_f**) and the second one having 1s for all the  rows for which the mushroom had bruises t (**bruises_t**). <br> <br>\n",
    "In the case of our labels (Y), we instead encode them. In our example, we have two different possible outcomes (edible or not). Therefore, we set the color of the first outcome equal to 0 and the second possible outcome equal to 1. And all the informations for this class will be contained in a single array (column). <br> <br>\n",
    "\n",
    "I decided not to adopt this same approch for the features (X), because some Machine Learning classifier might think that higher numbers are more important than lower ones and therefore would not give the same importance to the all the different categories in the feature (this doesn't instead happen when we encode the labels)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(['class'], axis = 1)\n",
    "Y = df['class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cap-shape_b</th>\n",
       "      <th>cap-shape_c</th>\n",
       "      <th>cap-shape_f</th>\n",
       "      <th>cap-shape_k</th>\n",
       "      <th>cap-shape_s</th>\n",
       "      <th>cap-shape_x</th>\n",
       "      <th>cap-surface_f</th>\n",
       "      <th>cap-surface_g</th>\n",
       "      <th>cap-surface_s</th>\n",
       "      <th>cap-surface_y</th>\n",
       "      <th>cap-color_b</th>\n",
       "      <th>cap-color_c</th>\n",
       "      <th>cap-color_e</th>\n",
       "      <th>cap-color_g</th>\n",
       "      <th>cap-color_n</th>\n",
       "      <th>cap-color_p</th>\n",
       "      <th>cap-color_r</th>\n",
       "      <th>cap-color_u</th>\n",
       "      <th>cap-color_w</th>\n",
       "      <th>cap-color_y</th>\n",
       "      <th>bruises_f</th>\n",
       "      <th>bruises_t</th>\n",
       "      <th>odor_a</th>\n",
       "      <th>odor_c</th>\n",
       "      <th>odor_f</th>\n",
       "      <th>odor_l</th>\n",
       "      <th>odor_m</th>\n",
       "      <th>odor_n</th>\n",
       "      <th>odor_p</th>\n",
       "      <th>odor_s</th>\n",
       "      <th>odor_y</th>\n",
       "      <th>gill-attachment_a</th>\n",
       "      <th>gill-attachment_f</th>\n",
       "      <th>gill-spacing_c</th>\n",
       "      <th>gill-spacing_w</th>\n",
       "      <th>gill-size_b</th>\n",
       "      <th>gill-size_n</th>\n",
       "      <th>gill-color_b</th>\n",
       "      <th>gill-color_e</th>\n",
       "      <th>gill-color_g</th>\n",
       "      <th>gill-color_h</th>\n",
       "      <th>gill-color_k</th>\n",
       "      <th>gill-color_n</th>\n",
       "      <th>gill-color_o</th>\n",
       "      <th>gill-color_p</th>\n",
       "      <th>gill-color_r</th>\n",
       "      <th>gill-color_u</th>\n",
       "      <th>gill-color_w</th>\n",
       "      <th>gill-color_y</th>\n",
       "      <th>stalk-shape_e</th>\n",
       "      <th>stalk-shape_t</th>\n",
       "      <th>stalk-root_?</th>\n",
       "      <th>stalk-root_b</th>\n",
       "      <th>stalk-root_c</th>\n",
       "      <th>stalk-root_e</th>\n",
       "      <th>stalk-root_r</th>\n",
       "      <th>stalk-surface-above-ring_f</th>\n",
       "      <th>stalk-surface-above-ring_k</th>\n",
       "      <th>stalk-surface-above-ring_s</th>\n",
       "      <th>stalk-surface-above-ring_y</th>\n",
       "      <th>stalk-surface-below-ring_f</th>\n",
       "      <th>stalk-surface-below-ring_k</th>\n",
       "      <th>stalk-surface-below-ring_s</th>\n",
       "      <th>stalk-surface-below-ring_y</th>\n",
       "      <th>stalk-color-above-ring_b</th>\n",
       "      <th>stalk-color-above-ring_c</th>\n",
       "      <th>stalk-color-above-ring_e</th>\n",
       "      <th>stalk-color-above-ring_g</th>\n",
       "      <th>stalk-color-above-ring_n</th>\n",
       "      <th>stalk-color-above-ring_o</th>\n",
       "      <th>stalk-color-above-ring_p</th>\n",
       "      <th>stalk-color-above-ring_w</th>\n",
       "      <th>stalk-color-above-ring_y</th>\n",
       "      <th>stalk-color-below-ring_b</th>\n",
       "      <th>stalk-color-below-ring_c</th>\n",
       "      <th>stalk-color-below-ring_e</th>\n",
       "      <th>stalk-color-below-ring_g</th>\n",
       "      <th>stalk-color-below-ring_n</th>\n",
       "      <th>stalk-color-below-ring_o</th>\n",
       "      <th>stalk-color-below-ring_p</th>\n",
       "      <th>stalk-color-below-ring_w</th>\n",
       "      <th>stalk-color-below-ring_y</th>\n",
       "      <th>veil-type_p</th>\n",
       "      <th>veil-color_n</th>\n",
       "      <th>veil-color_o</th>\n",
       "      <th>veil-color_w</th>\n",
       "      <th>veil-color_y</th>\n",
       "      <th>ring-number_n</th>\n",
       "      <th>ring-number_o</th>\n",
       "      <th>ring-number_t</th>\n",
       "      <th>ring-type_e</th>\n",
       "      <th>ring-type_f</th>\n",
       "      <th>ring-type_l</th>\n",
       "      <th>ring-type_n</th>\n",
       "      <th>ring-type_p</th>\n",
       "      <th>spore-print-color_b</th>\n",
       "      <th>spore-print-color_h</th>\n",
       "      <th>spore-print-color_k</th>\n",
       "      <th>spore-print-color_n</th>\n",
       "      <th>spore-print-color_o</th>\n",
       "      <th>spore-print-color_r</th>\n",
       "      <th>spore-print-color_u</th>\n",
       "      <th>spore-print-color_w</th>\n",
       "      <th>spore-print-color_y</th>\n",
       "      <th>population_a</th>\n",
       "      <th>population_c</th>\n",
       "      <th>population_n</th>\n",
       "      <th>population_s</th>\n",
       "      <th>population_v</th>\n",
       "      <th>population_y</th>\n",
       "      <th>habitat_d</th>\n",
       "      <th>habitat_g</th>\n",
       "      <th>habitat_l</th>\n",
       "      <th>habitat_m</th>\n",
       "      <th>habitat_p</th>\n",
       "      <th>habitat_u</th>\n",
       "      <th>habitat_w</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cap-shape_b  cap-shape_c  cap-shape_f  cap-shape_k  cap-shape_s  \\\n",
       "0            0            0            0            0            0   \n",
       "1            0            0            0            0            0   \n",
       "2            1            0            0            0            0   \n",
       "3            0            0            0            0            0   \n",
       "4            0            0            0            0            0   \n",
       "\n",
       "   cap-shape_x  cap-surface_f  cap-surface_g  cap-surface_s  cap-surface_y  \\\n",
       "0            1              0              0              1              0   \n",
       "1            1              0              0              1              0   \n",
       "2            0              0              0              1              0   \n",
       "3            1              0              0              0              1   \n",
       "4            1              0              0              1              0   \n",
       "\n",
       "   cap-color_b  cap-color_c  cap-color_e  cap-color_g  cap-color_n  \\\n",
       "0            0            0            0            0            1   \n",
       "1            0            0            0            0            0   \n",
       "2            0            0            0            0            0   \n",
       "3            0            0            0            0            0   \n",
       "4            0            0            0            1            0   \n",
       "\n",
       "   cap-color_p  cap-color_r  cap-color_u  cap-color_w  cap-color_y  bruises_f  \\\n",
       "0            0            0            0            0            0          0   \n",
       "1            0            0            0            0            1          0   \n",
       "2            0            0            0            1            0          0   \n",
       "3            0            0            0            1            0          0   \n",
       "4            0            0            0            0            0          1   \n",
       "\n",
       "   bruises_t  odor_a  odor_c  odor_f  odor_l  odor_m  odor_n  odor_p  odor_s  \\\n",
       "0          1       0       0       0       0       0       0       1       0   \n",
       "1          1       1       0       0       0       0       0       0       0   \n",
       "2          1       0       0       0       1       0       0       0       0   \n",
       "3          1       0       0       0       0       0       0       1       0   \n",
       "4          0       0       0       0       0       0       1       0       0   \n",
       "\n",
       "   odor_y  gill-attachment_a  gill-attachment_f  gill-spacing_c  \\\n",
       "0       0                  0                  1               1   \n",
       "1       0                  0                  1               1   \n",
       "2       0                  0                  1               1   \n",
       "3       0                  0                  1               1   \n",
       "4       0                  0                  1               0   \n",
       "\n",
       "   gill-spacing_w  gill-size_b  gill-size_n  gill-color_b  gill-color_e  \\\n",
       "0               0            0            1             0             0   \n",
       "1               0            1            0             0             0   \n",
       "2               0            1            0             0             0   \n",
       "3               0            0            1             0             0   \n",
       "4               1            1            0             0             0   \n",
       "\n",
       "   gill-color_g  gill-color_h  gill-color_k  gill-color_n  gill-color_o  \\\n",
       "0             0             0             1             0             0   \n",
       "1             0             0             1             0             0   \n",
       "2             0             0             0             1             0   \n",
       "3             0             0             0             1             0   \n",
       "4             0             0             1             0             0   \n",
       "\n",
       "   gill-color_p  gill-color_r  gill-color_u  gill-color_w  gill-color_y  \\\n",
       "0             0             0             0             0             0   \n",
       "1             0             0             0             0             0   \n",
       "2             0             0             0             0             0   \n",
       "3             0             0             0             0             0   \n",
       "4             0             0             0             0             0   \n",
       "\n",
       "   stalk-shape_e  stalk-shape_t  stalk-root_?  stalk-root_b  stalk-root_c  \\\n",
       "0              1              0             0             0             0   \n",
       "1              1              0             0             0             1   \n",
       "2              1              0             0             0             1   \n",
       "3              1              0             0             0             0   \n",
       "4              0              1             0             0             0   \n",
       "\n",
       "   stalk-root_e  stalk-root_r  stalk-surface-above-ring_f  \\\n",
       "0             1             0                           0   \n",
       "1             0             0                           0   \n",
       "2             0             0                           0   \n",
       "3             1             0                           0   \n",
       "4             1             0                           0   \n",
       "\n",
       "   stalk-surface-above-ring_k  stalk-surface-above-ring_s  \\\n",
       "0                           0                           1   \n",
       "1                           0                           1   \n",
       "2                           0                           1   \n",
       "3                           0                           1   \n",
       "4                           0                           1   \n",
       "\n",
       "   stalk-surface-above-ring_y  stalk-surface-below-ring_f  \\\n",
       "0                           0                           0   \n",
       "1                           0                           0   \n",
       "2                           0                           0   \n",
       "3                           0                           0   \n",
       "4                           0                           0   \n",
       "\n",
       "   stalk-surface-below-ring_k  stalk-surface-below-ring_s  \\\n",
       "0                           0                           1   \n",
       "1                           0                           1   \n",
       "2                           0                           1   \n",
       "3                           0                           1   \n",
       "4                           0                           1   \n",
       "\n",
       "   stalk-surface-below-ring_y  stalk-color-above-ring_b  \\\n",
       "0                           0                         0   \n",
       "1                           0                         0   \n",
       "2                           0                         0   \n",
       "3                           0                         0   \n",
       "4                           0                         0   \n",
       "\n",
       "   stalk-color-above-ring_c  stalk-color-above-ring_e  \\\n",
       "0                         0                         0   \n",
       "1                         0                         0   \n",
       "2                         0                         0   \n",
       "3                         0                         0   \n",
       "4                         0                         0   \n",
       "\n",
       "   stalk-color-above-ring_g  stalk-color-above-ring_n  \\\n",
       "0                         0                         0   \n",
       "1                         0                         0   \n",
       "2                         0                         0   \n",
       "3                         0                         0   \n",
       "4                         0                         0   \n",
       "\n",
       "   stalk-color-above-ring_o  stalk-color-above-ring_p  \\\n",
       "0                         0                         0   \n",
       "1                         0                         0   \n",
       "2                         0                         0   \n",
       "3                         0                         0   \n",
       "4                         0                         0   \n",
       "\n",
       "   stalk-color-above-ring_w  stalk-color-above-ring_y  \\\n",
       "0                         1                         0   \n",
       "1                         1                         0   \n",
       "2                         1                         0   \n",
       "3                         1                         0   \n",
       "4                         1                         0   \n",
       "\n",
       "   stalk-color-below-ring_b  stalk-color-below-ring_c  \\\n",
       "0                         0                         0   \n",
       "1                         0                         0   \n",
       "2                         0                         0   \n",
       "3                         0                         0   \n",
       "4                         0                         0   \n",
       "\n",
       "   stalk-color-below-ring_e  stalk-color-below-ring_g  \\\n",
       "0                         0                         0   \n",
       "1                         0                         0   \n",
       "2                         0                         0   \n",
       "3                         0                         0   \n",
       "4                         0                         0   \n",
       "\n",
       "   stalk-color-below-ring_n  stalk-color-below-ring_o  \\\n",
       "0                         0                         0   \n",
       "1                         0                         0   \n",
       "2                         0                         0   \n",
       "3                         0                         0   \n",
       "4                         0                         0   \n",
       "\n",
       "   stalk-color-below-ring_p  stalk-color-below-ring_w  \\\n",
       "0                         0                         1   \n",
       "1                         0                         1   \n",
       "2                         0                         1   \n",
       "3                         0                         1   \n",
       "4                         0                         1   \n",
       "\n",
       "   stalk-color-below-ring_y  veil-type_p  veil-color_n  veil-color_o  \\\n",
       "0                         0            1             0             0   \n",
       "1                         0            1             0             0   \n",
       "2                         0            1             0             0   \n",
       "3                         0            1             0             0   \n",
       "4                         0            1             0             0   \n",
       "\n",
       "   veil-color_w  veil-color_y  ring-number_n  ring-number_o  ring-number_t  \\\n",
       "0             1             0              0              1              0   \n",
       "1             1             0              0              1              0   \n",
       "2             1             0              0              1              0   \n",
       "3             1             0              0              1              0   \n",
       "4             1             0              0              1              0   \n",
       "\n",
       "   ring-type_e  ring-type_f  ring-type_l  ring-type_n  ring-type_p  \\\n",
       "0            0            0            0            0            1   \n",
       "1            0            0            0            0            1   \n",
       "2            0            0            0            0            1   \n",
       "3            0            0            0            0            1   \n",
       "4            1            0            0            0            0   \n",
       "\n",
       "   spore-print-color_b  spore-print-color_h  spore-print-color_k  \\\n",
       "0                    0                    0                    1   \n",
       "1                    0                    0                    0   \n",
       "2                    0                    0                    0   \n",
       "3                    0                    0                    1   \n",
       "4                    0                    0                    0   \n",
       "\n",
       "   spore-print-color_n  spore-print-color_o  spore-print-color_r  \\\n",
       "0                    0                    0                    0   \n",
       "1                    1                    0                    0   \n",
       "2                    1                    0                    0   \n",
       "3                    0                    0                    0   \n",
       "4                    1                    0                    0   \n",
       "\n",
       "   spore-print-color_u  spore-print-color_w  spore-print-color_y  \\\n",
       "0                    0                    0                    0   \n",
       "1                    0                    0                    0   \n",
       "2                    0                    0                    0   \n",
       "3                    0                    0                    0   \n",
       "4                    0                    0                    0   \n",
       "\n",
       "   population_a  population_c  population_n  population_s  population_v  \\\n",
       "0             0             0             0             1             0   \n",
       "1             0             0             1             0             0   \n",
       "2             0             0             1             0             0   \n",
       "3             0             0             0             1             0   \n",
       "4             1             0             0             0             0   \n",
       "\n",
       "   population_y  habitat_d  habitat_g  habitat_l  habitat_m  habitat_p  \\\n",
       "0             0          0          0          0          0          0   \n",
       "1             0          0          1          0          0          0   \n",
       "2             0          0          0          0          1          0   \n",
       "3             0          0          0          0          0          0   \n",
       "4             0          0          1          0          0          0   \n",
       "\n",
       "   habitat_u  habitat_w  \n",
       "0          1          0  \n",
       "1          0          0  \n",
       "2          0          0  \n",
       "3          1          0  \n",
       "4          0          0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = pd.get_dummies(X, prefix_sep='_')\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "117"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, ..., 0, 1, 0])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = LabelEncoder().fit_transform(Y)\n",
    "#np.set_printoptions(threshold=np.inf)\n",
    "Y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Machine Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report,confusion_matrix\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import svm\n",
    "from sklearn import tree\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "X2 = StandardScaler().fit_transform(X)\n",
    "\n",
    "X_Train, X_Test, Y_Train, Y_Test = train_test_split(X2, Y, test_size = 0.30, random_state = 101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.24604894200000027\n",
      "[[1274    0]\n",
      " [   0 1164]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00      1274\n",
      "           1       1.00      1.00      1.00      1164\n",
      "\n",
      "    accuracy                           1.00      2438\n",
      "   macro avg       1.00      1.00      1.00      2438\n",
      "weighted avg       1.00      1.00      1.00      2438\n",
      "\n"
     ]
    }
   ],
   "source": [
    "start = time.process_time()\n",
    "trainedmodel = LogisticRegression().fit(X_Train,Y_Train)\n",
    "print(time.process_time() - start)\n",
    "predictions =trainedmodel.predict(X_Test)\n",
    "print(confusion_matrix(Y_Test,predictions))\n",
    "print(classification_report(Y_Test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4504241269999998\n",
      "[[1274    0]\n",
      " [   0 1164]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00      1274\n",
      "           1       1.00      1.00      1.00      1164\n",
      "\n",
      "    accuracy                           1.00      2438\n",
      "   macro avg       1.00      1.00      1.00      2438\n",
      "weighted avg       1.00      1.00      1.00      2438\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/sklearn/svm/base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "start = time.process_time()\n",
    "trainedsvm = svm.LinearSVC().fit(X_Train, Y_Train)\n",
    "print(time.process_time() - start)\n",
    "predictionsvm = trainedsvm.predict(X_Test)\n",
    "print(confusion_matrix(Y_Test,predictionsvm))\n",
    "print(classification_report(Y_Test,predictionsvm))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.02882629099999967\n",
      "[[1274    0]\n",
      " [   0 1164]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00      1274\n",
      "           1       1.00      1.00      1.00      1164\n",
      "\n",
      "    accuracy                           1.00      2438\n",
      "   macro avg       1.00      1.00      1.00      2438\n",
      "weighted avg       1.00      1.00      1.00      2438\n",
      "\n"
     ]
    }
   ],
   "source": [
    "start = time.process_time()\n",
    "trainedtree = tree.DecisionTreeClassifier().fit(X_Train, Y_Train)\n",
    "print(time.process_time() - start)\n",
    "predictionstree = trainedtree.predict(X_Test)\n",
    "print(confusion_matrix(Y_Test,predictionstree))\n",
    "print(classification_report(Y_Test,predictionstree))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "<text text-anchor=\"start\" x=\"337\" y=\"-377.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">odor_n ≤ 0.133</text>\n",
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       "<text text-anchor=\"start\" x=\"203\" y=\"-258.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">stalk&#45;root_c ≤ 1.709</text>\n",
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       "<text text-anchor=\"start\" x=\"468.5\" y=\"-243.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.062</text>\n",
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       "<text text-anchor=\"start\" x=\"227.5\" y=\"-124.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.118</text>\n",
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       "<text text-anchor=\"start\" x=\"400\" y=\"-139.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">stalk&#45;surface&#45;below&#45;ring_y ≤ 2.532</text>\n",
       "<text text-anchor=\"start\" x=\"468.5\" y=\"-124.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.029</text>\n",
       "<text text-anchor=\"start\" x=\"455.5\" y=\"-109.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 2429</text>\n",
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       "<text text-anchor=\"start\" x=\"462.5\" y=\"-79.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = edible</text>\n",
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      ],
      "text/plain": [
       "<graphviz.files.Source at 0x7f2dea051710>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import graphviz\n",
    "from sklearn.tree import DecisionTreeClassifier, export_graphviz\n",
    "\n",
    "data = export_graphviz(trainedtree,out_file=None,feature_names= X.columns,\n",
    "                       class_names=['edible', 'poisonous'],  \n",
    "                       filled=True, rounded=True,  \n",
    "                       max_depth=2,\n",
    "                       special_characters=True)\n",
    "graph = graphviz.Source(data)\n",
    "graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are many different methods which can be applied for Feature Selection. Some of the most important ones are: <br>\n",
    "1. Filter Method = filtering our dataset and taking only a subset of it containg all the relevant features (eg. correlation matrix using Pearson Correlation)\n",
    "2. Wrapper Method = follows the same objective of the FIlter Method but uses a Machine Learning model as it's evaluation criteria (eg. Forward/Backward/Bidirectional/Recursive Feature Elimination). We feed some features to our Machine Learning model, evaluate their performance and then decide if add or remove feature to increase accuracy. As a result, this mothod can be more accurate than filtering, is more computationally expensive.\n",
    "3. Embedded Method = like the FIlter Method also the Embedded Method makes use of a Machine Learning model. The difference between the two different methods is that the Embedded Method examines the different training iterations of our ML model and then ranks the importance of each feature based on how much each of the features contributed to the ML model training (eg. LASSO Regularization)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feature Importance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Decision Trees models which are based on ensembles (eg. Extra Trees and Random Forest) can be used to rank the importaqnce of the different features. Knowing which features our model is giving most importance can be of vital importance to understand how our model is making it's predictions (therefore making it more explainable). At the same time, we can get rid of the features which do not bring any benefit to our model (our confuse it to make a wrong decision!)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.2676709799999992\n",
      "[[1274    0]\n",
      " [   0 1164]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00      1274\n",
      "           1       1.00      1.00      1.00      1164\n",
      "\n",
      "    accuracy                           1.00      2438\n",
      "   macro avg       1.00      1.00      1.00      2438\n",
      "weighted avg       1.00      1.00      1.00      2438\n",
      "\n"
     ]
    }
   ],
   "source": [
    "start = time.process_time()\n",
    "trainedforest = RandomForestClassifier(n_estimators=700).fit(X_Train,Y_Train)\n",
    "print(time.process_time() - start)\n",
    "predictionforest = trainedforest.predict(X_Test)\n",
    "print(confusion_matrix(Y_Test,predictionforest))\n",
    "print(classification_report(Y_Test,predictionforest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2dea001eb8>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x1760 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(num=None, figsize=(20, 22), dpi=80, facecolor='w', edgecolor='k')\n",
    "\n",
    "feat_importances = pd.Series(trainedforest.feature_importances_, index= X.columns)\n",
    "feat_importances.nlargest(19).plot(kind='barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_Reduced = X[['odor_n','odor_f', 'gill-size_n','gill-size_b']]\n",
    "X_Reduced = StandardScaler().fit_transform(X_Reduced)\n",
    "X_Train2, X_Test2, Y_Train2, Y_Test2 = train_test_split(X_Reduced, Y, test_size = 0.30, random_state = 101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.1874146949999993\n",
      "[[1248   26]\n",
      " [  53 1111]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.98      0.97      1274\n",
      "           1       0.98      0.95      0.97      1164\n",
      "\n",
      "    accuracy                           0.97      2438\n",
      "   macro avg       0.97      0.97      0.97      2438\n",
      "weighted avg       0.97      0.97      0.97      2438\n",
      "\n"
     ]
    }
   ],
   "source": [
    "start = time.process_time()\n",
    "trainedforest = RandomForestClassifier(n_estimators=700).fit(X_Train2,Y_Train2)\n",
    "print(time.process_time() - start)\n",
    "predictionforest = trainedforest.predict(X_Test2)\n",
    "print(confusion_matrix(Y_Test2,predictionforest))\n",
    "print(classification_report(Y_Test2,predictionforest))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Recursive Feature Elimination"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recursive Feature Elimination (RFE) takes as input the instance of a Machine Learning model and the final desired number of features to use. It then recursively reduces the number of features to use by ranking them using the Machine Learning model accuracy as metrics. Creating a for loop in which the number of input features is our variable, it could then be possible to found out the optimal number of features our model needs by keeping track of the accuracy registred in each loop iteration. Using RFE support_ method, we can then find out the names of the features which have been evaluated as most important (rfe.support_ return a boolean list in which TRUE represent that a feature is considered as important and FALSE represent that a feature is not considered important).  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "210.85839133899998\n",
      "Overall Accuracy using RFE:  0.9675963904840033\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_selection import RFE\n",
    "\n",
    "model = RandomForestClassifier(n_estimators=700)\n",
    "rfe = RFE(model, 4)\n",
    "start = time.process_time()\n",
    "RFE_X_Train = rfe.fit_transform(X_Train,Y_Train)\n",
    "RFE_X_Test = rfe.transform(X_Test)\n",
    "rfe = rfe.fit(RFE_X_Train,Y_Train)\n",
    "print(time.process_time() - start)\n",
    "print(\"Overall Accuracy using RFE: \", rfe.score(RFE_X_Test,Y_Test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Features:  4\n",
      "Selected Features: \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Index(['odor_f', 'odor_n', 'gill-size_b', 'gill-size_n'], dtype='object')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = RandomForestClassifier(n_estimators=700)\n",
    "rfe = RFE(model, 4)\n",
    "RFE_X_Train = rfe.fit_transform(X_Train,Y_Train)\n",
    "model.fit(RFE_X_Train,Y_Train) \n",
    "print(\"Number of Features: \", rfe.n_features_)\n",
    "print(\"Selected Features: \")\n",
    "colcheck = pd.Series(rfe.support_,index = list(X.columns))\n",
    "colcheck[colcheck == True].index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SelectFromModel: Meta-transformer for selecting features based on importance weights."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SelectFromModel is another Scikit-learn method which can be used for Feature Selection. This method can be used with all the different types of Scikit-learn models (after fitting) which have a coef_ or feature_importances_ attribute. Compared to RFE, SelectFromModel is a less robust solution. In fact, SelectFromModel just removes less important features based on a calculated threshold (no optimization iteration process involved). <br>\n",
    "\n",
    "In order to test SelectFromModel efficacy, I decided to use an ExtraTreesClassifier in this example. ExtraTreesClassifier (Extremely Randomized Trees) is tree based ensamble classifier which can yield less variance compared to Random Forest methods (reducing therefore the risk of overfitting). The main difference between Random Forest and Extremely Randomized Trees is that in Extremely Randomized Trees nodes are sampled without replacement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.04993879400001333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(5686, 30)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "\n",
    "model = ExtraTreesClassifier()\n",
    "start = time.process_time()\n",
    "model = model.fit(X_Train,Y_Train)\n",
    "model = SelectFromModel(model, prefit=True)\n",
    "print(time.process_time() - start)\n",
    "Selected_X = model.transform(X_Train)\n",
    "Selected_X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.6003950479999958\n",
      "[[1274    0]\n",
      " [   0 1164]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00      1274\n",
      "           1       1.00      1.00      1.00      1164\n",
      "\n",
      "    accuracy                           1.00      2438\n",
      "   macro avg       1.00      1.00      1.00      2438\n",
      "weighted avg       1.00      1.00      1.00      2438\n",
      "\n"
     ]
    }
   ],
   "source": [
    "start = time.process_time()\n",
    "trainedforest = RandomForestClassifier(n_estimators=700).fit(Selected_X, Y_Train)\n",
    "print(time.process_time() - start)\n",
    "Selected_X_Test = model.transform(X_Test)\n",
    "predictionforest = trainedforest.predict(Selected_X_Test)\n",
    "print(confusion_matrix(Y_Test,predictionforest))\n",
    "print(classification_report(Y_Test,predictionforest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature ranking:\n",
      "1. feature 5 (0.189944)\n",
      "2. feature 3 (0.114456)\n",
      "3. feature 9 (0.077251)\n",
      "4. feature 8 (0.072935)\n",
      "5. feature 16 (0.065652)\n",
      "6. feature 26 (0.049318)\n",
      "7. feature 10 (0.044246)\n",
      "8. feature 19 (0.044056)\n",
      "9. feature 25 (0.038260)\n",
      "10. feature 1 (0.033690)\n",
      "11. feature 2 (0.027526)\n",
      "12. feature 29 (0.023776)\n",
      "13. feature 27 (0.022094)\n",
      "14. feature 11 (0.021892)\n",
      "15. feature 14 (0.021881)\n",
      "16. feature 13 (0.021213)\n",
      "17. feature 7 (0.020086)\n",
      "18. feature 17 (0.017630)\n",
      "19. feature 6 (0.015564)\n",
      "20. feature 24 (0.011387)\n",
      "21. feature 12 (0.011129)\n",
      "22. feature 4 (0.010294)\n",
      "23. feature 23 (0.008828)\n",
      "24. feature 18 (0.008126)\n",
      "25. feature 21 (0.006767)\n",
      "26. feature 22 (0.006717)\n",
      "27. feature 28 (0.005723)\n",
      "28. feature 15 (0.005432)\n",
      "29. feature 0 (0.003001)\n",
      "30. feature 20 (0.001125)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# https://scikit-learn.org/stable/auto_examples/ensemble/plot_forest_importances.html\n",
    "importances = trainedforest.feature_importances_\n",
    "std = np.std([tree.feature_importances_ for tree in trainedforest.estimators_],\n",
    "             axis=0)\n",
    "indices = np.argsort(importances)[::-1]\n",
    "\n",
    "# Print the feature ranking\n",
    "print(\"Feature ranking:\")\n",
    "\n",
    "for f in range(Selected_X.shape[1]):\n",
    "    print(\"%d. feature %d (%f)\" % (f + 1, indices[f], importances[indices[f]]))\n",
    "\n",
    "# Plot the feature importances of the forest\n",
    "plt.figure()\n",
    "plt.title(\"Feature importances\")\n",
    "plt.bar(range(Selected_X.shape[1]), importances[indices],\n",
    "       color=\"r\", yerr=std[indices], align=\"center\")\n",
    "plt.xticks(range(Selected_X.shape[1]), indices)\n",
    "plt.xlim([-1, Selected_X.shape[1]])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Correlation Matrix Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using Seaborn, we can now plot the Pearson correlation heatmap of our dataset. Inspecting this plot, we can then be able to see the correlation of our independent variables (X) with our label (Y). Finally, we can then select just the features which are most correlated with Y and train/test an SVM model to test the results of this approach."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using Pearson correlation our returned coefficient values will vary between -1 and 1:\n",
    "- If the correlation between two features is 0 this means that changing any of these two features will not affect the other.\n",
    "- If the correlation between two features is greater than 0 this means that increating the values in one feature will make increase also the values in the other feature (the closer the correlation coefficient is to 1 and the stronger is going to be this bond between the two different features). \n",
    "- If the correlation between two features is less than 0 this means that increating the values in one feature will make decrease the values in the other feature (the closer the correlation coefficient is to -1 and the stronger is going to be this relationship between the two different features). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Another possible aspect to control in this analysis would be to check if the selected variables are highly correlated each other. If they are, we would then need to keep just one of the correlated ones and drop the others."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cap-shape_b</th>\n",
       "      <th>cap-shape_c</th>\n",
       "      <th>cap-shape_f</th>\n",
       "      <th>cap-shape_k</th>\n",
       "      <th>cap-shape_s</th>\n",
       "      <th>cap-shape_x</th>\n",
       "      <th>cap-surface_f</th>\n",
       "      <th>cap-surface_g</th>\n",
       "      <th>cap-surface_s</th>\n",
       "      <th>cap-surface_y</th>\n",
       "      <th>cap-color_b</th>\n",
       "      <th>cap-color_c</th>\n",
       "      <th>cap-color_e</th>\n",
       "      <th>cap-color_g</th>\n",
       "      <th>cap-color_n</th>\n",
       "      <th>cap-color_p</th>\n",
       "      <th>cap-color_r</th>\n",
       "      <th>cap-color_u</th>\n",
       "      <th>cap-color_w</th>\n",
       "      <th>cap-color_y</th>\n",
       "      <th>bruises_f</th>\n",
       "      <th>bruises_t</th>\n",
       "      <th>odor_a</th>\n",
       "      <th>odor_c</th>\n",
       "      <th>odor_f</th>\n",
       "      <th>odor_l</th>\n",
       "      <th>odor_m</th>\n",
       "      <th>odor_n</th>\n",
       "      <th>odor_p</th>\n",
       "      <th>odor_s</th>\n",
       "      <th>odor_y</th>\n",
       "      <th>gill-attachment_a</th>\n",
       "      <th>gill-attachment_f</th>\n",
       "      <th>gill-spacing_c</th>\n",
       "      <th>gill-spacing_w</th>\n",
       "      <th>gill-size_b</th>\n",
       "      <th>gill-size_n</th>\n",
       "      <th>gill-color_b</th>\n",
       "      <th>gill-color_e</th>\n",
       "      <th>gill-color_g</th>\n",
       "      <th>gill-color_h</th>\n",
       "      <th>gill-color_k</th>\n",
       "      <th>gill-color_n</th>\n",
       "      <th>gill-color_o</th>\n",
       "      <th>gill-color_p</th>\n",
       "      <th>gill-color_r</th>\n",
       "      <th>gill-color_u</th>\n",
       "      <th>gill-color_w</th>\n",
       "      <th>gill-color_y</th>\n",
       "      <th>stalk-shape_e</th>\n",
       "      <th>stalk-shape_t</th>\n",
       "      <th>stalk-root_?</th>\n",
       "      <th>stalk-root_b</th>\n",
       "      <th>stalk-root_c</th>\n",
       "      <th>stalk-root_e</th>\n",
       "      <th>stalk-root_r</th>\n",
       "      <th>stalk-surface-above-ring_f</th>\n",
       "      <th>stalk-surface-above-ring_k</th>\n",
       "      <th>stalk-surface-above-ring_s</th>\n",
       "      <th>stalk-surface-above-ring_y</th>\n",
       "      <th>stalk-surface-below-ring_f</th>\n",
       "      <th>stalk-surface-below-ring_k</th>\n",
       "      <th>stalk-surface-below-ring_s</th>\n",
       "      <th>stalk-surface-below-ring_y</th>\n",
       "      <th>stalk-color-above-ring_b</th>\n",
       "      <th>stalk-color-above-ring_c</th>\n",
       "      <th>stalk-color-above-ring_e</th>\n",
       "      <th>stalk-color-above-ring_g</th>\n",
       "      <th>stalk-color-above-ring_n</th>\n",
       "      <th>stalk-color-above-ring_o</th>\n",
       "      <th>stalk-color-above-ring_p</th>\n",
       "      <th>stalk-color-above-ring_w</th>\n",
       "      <th>stalk-color-above-ring_y</th>\n",
       "      <th>stalk-color-below-ring_b</th>\n",
       "      <th>stalk-color-below-ring_c</th>\n",
       "      <th>stalk-color-below-ring_e</th>\n",
       "      <th>stalk-color-below-ring_g</th>\n",
       "      <th>stalk-color-below-ring_n</th>\n",
       "      <th>stalk-color-below-ring_o</th>\n",
       "      <th>stalk-color-below-ring_p</th>\n",
       "      <th>stalk-color-below-ring_w</th>\n",
       "      <th>stalk-color-below-ring_y</th>\n",
       "      <th>veil-type_p</th>\n",
       "      <th>veil-color_n</th>\n",
       "      <th>veil-color_o</th>\n",
       "      <th>veil-color_w</th>\n",
       "      <th>veil-color_y</th>\n",
       "      <th>ring-number_n</th>\n",
       "      <th>ring-number_o</th>\n",
       "      <th>ring-number_t</th>\n",
       "      <th>ring-type_e</th>\n",
       "      <th>ring-type_f</th>\n",
       "      <th>ring-type_l</th>\n",
       "      <th>ring-type_n</th>\n",
       "      <th>ring-type_p</th>\n",
       "      <th>spore-print-color_b</th>\n",
       "      <th>spore-print-color_h</th>\n",
       "      <th>spore-print-color_k</th>\n",
       "      <th>spore-print-color_n</th>\n",
       "      <th>spore-print-color_o</th>\n",
       "      <th>spore-print-color_r</th>\n",
       "      <th>spore-print-color_u</th>\n",
       "      <th>spore-print-color_w</th>\n",
       "      <th>spore-print-color_y</th>\n",
       "      <th>population_a</th>\n",
       "      <th>population_c</th>\n",
       "      <th>population_n</th>\n",
       "      <th>population_s</th>\n",
       "      <th>population_v</th>\n",
       "      <th>population_y</th>\n",
       "      <th>habitat_d</th>\n",
       "      <th>habitat_g</th>\n",
       "      <th>habitat_l</th>\n",
       "      <th>habitat_m</th>\n",
       "      <th>habitat_p</th>\n",
       "      <th>habitat_u</th>\n",
       "      <th>habitat_w</th>\n",
       "      <th>Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cap-shape_b  cap-shape_c  cap-shape_f  cap-shape_k  cap-shape_s  \\\n",
       "0            0            0            0            0            0   \n",
       "1            0            0            0            0            0   \n",
       "2            1            0            0            0            0   \n",
       "3            0            0            0            0            0   \n",
       "4            0            0            0            0            0   \n",
       "\n",
       "   cap-shape_x  cap-surface_f  cap-surface_g  cap-surface_s  cap-surface_y  \\\n",
       "0            1              0              0              1              0   \n",
       "1            1              0              0              1              0   \n",
       "2            0              0              0              1              0   \n",
       "3            1              0              0              0              1   \n",
       "4            1              0              0              1              0   \n",
       "\n",
       "   cap-color_b  cap-color_c  cap-color_e  cap-color_g  cap-color_n  \\\n",
       "0            0            0            0            0            1   \n",
       "1            0            0            0            0            0   \n",
       "2            0            0            0            0            0   \n",
       "3            0            0            0            0            0   \n",
       "4            0            0            0            1            0   \n",
       "\n",
       "   cap-color_p  cap-color_r  cap-color_u  cap-color_w  cap-color_y  bruises_f  \\\n",
       "0            0            0            0            0            0          0   \n",
       "1            0            0            0            0            1          0   \n",
       "2            0            0            0            1            0          0   \n",
       "3            0            0            0            1            0          0   \n",
       "4            0            0            0            0            0          1   \n",
       "\n",
       "   bruises_t  odor_a  odor_c  odor_f  odor_l  odor_m  odor_n  odor_p  odor_s  \\\n",
       "0          1       0       0       0       0       0       0       1       0   \n",
       "1          1       1       0       0       0       0       0       0       0   \n",
       "2          1       0       0       0       1       0       0       0       0   \n",
       "3          1       0       0       0       0       0       0       1       0   \n",
       "4          0       0       0       0       0       0       1       0       0   \n",
       "\n",
       "   odor_y  gill-attachment_a  gill-attachment_f  gill-spacing_c  \\\n",
       "0       0                  0                  1               1   \n",
       "1       0                  0                  1               1   \n",
       "2       0                  0                  1               1   \n",
       "3       0                  0                  1               1   \n",
       "4       0                  0                  1               0   \n",
       "\n",
       "   gill-spacing_w  gill-size_b  gill-size_n  gill-color_b  gill-color_e  \\\n",
       "0               0            0            1             0             0   \n",
       "1               0            1            0             0             0   \n",
       "2               0            1            0             0             0   \n",
       "3               0            0            1             0             0   \n",
       "4               1            1            0             0             0   \n",
       "\n",
       "   gill-color_g  gill-color_h  gill-color_k  gill-color_n  gill-color_o  \\\n",
       "0             0             0             1             0             0   \n",
       "1             0             0             1             0             0   \n",
       "2             0             0             0             1             0   \n",
       "3             0             0             0             1             0   \n",
       "4             0             0             1             0             0   \n",
       "\n",
       "   gill-color_p  gill-color_r  gill-color_u  gill-color_w  gill-color_y  \\\n",
       "0             0             0             0             0             0   \n",
       "1             0             0             0             0             0   \n",
       "2             0             0             0             0             0   \n",
       "3             0             0             0             0             0   \n",
       "4             0             0             0             0             0   \n",
       "\n",
       "   stalk-shape_e  stalk-shape_t  stalk-root_?  stalk-root_b  stalk-root_c  \\\n",
       "0              1              0             0             0             0   \n",
       "1              1              0             0             0             1   \n",
       "2              1              0             0             0             1   \n",
       "3              1              0             0             0             0   \n",
       "4              0              1             0             0             0   \n",
       "\n",
       "   stalk-root_e  stalk-root_r  stalk-surface-above-ring_f  \\\n",
       "0             1             0                           0   \n",
       "1             0             0                           0   \n",
       "2             0             0                           0   \n",
       "3             1             0                           0   \n",
       "4             1             0                           0   \n",
       "\n",
       "   stalk-surface-above-ring_k  stalk-surface-above-ring_s  \\\n",
       "0                           0                           1   \n",
       "1                           0                           1   \n",
       "2                           0                           1   \n",
       "3                           0                           1   \n",
       "4                           0                           1   \n",
       "\n",
       "   stalk-surface-above-ring_y  stalk-surface-below-ring_f  \\\n",
       "0                           0                           0   \n",
       "1                           0                           0   \n",
       "2                           0                           0   \n",
       "3                           0                           0   \n",
       "4                           0                           0   \n",
       "\n",
       "   stalk-surface-below-ring_k  stalk-surface-below-ring_s  \\\n",
       "0                           0                           1   \n",
       "1                           0                           1   \n",
       "2                           0                           1   \n",
       "3                           0                           1   \n",
       "4                           0                           1   \n",
       "\n",
       "   stalk-surface-below-ring_y  stalk-color-above-ring_b  \\\n",
       "0                           0                         0   \n",
       "1                           0                         0   \n",
       "2                           0                         0   \n",
       "3                           0                         0   \n",
       "4                           0                         0   \n",
       "\n",
       "   stalk-color-above-ring_c  stalk-color-above-ring_e  \\\n",
       "0                         0                         0   \n",
       "1                         0                         0   \n",
       "2                         0                         0   \n",
       "3                         0                         0   \n",
       "4                         0                         0   \n",
       "\n",
       "   stalk-color-above-ring_g  stalk-color-above-ring_n  \\\n",
       "0                         0                         0   \n",
       "1                         0                         0   \n",
       "2                         0                         0   \n",
       "3                         0                         0   \n",
       "4                         0                         0   \n",
       "\n",
       "   stalk-color-above-ring_o  stalk-color-above-ring_p  \\\n",
       "0                         0                         0   \n",
       "1                         0                         0   \n",
       "2                         0                         0   \n",
       "3                         0                         0   \n",
       "4                         0                         0   \n",
       "\n",
       "   stalk-color-above-ring_w  stalk-color-above-ring_y  \\\n",
       "0                         1                         0   \n",
       "1                         1                         0   \n",
       "2                         1                         0   \n",
       "3                         1                         0   \n",
       "4                         1                         0   \n",
       "\n",
       "   stalk-color-below-ring_b  stalk-color-below-ring_c  \\\n",
       "0                         0                         0   \n",
       "1                         0                         0   \n",
       "2                         0                         0   \n",
       "3                         0                         0   \n",
       "4                         0                         0   \n",
       "\n",
       "   stalk-color-below-ring_e  stalk-color-below-ring_g  \\\n",
       "0                         0                         0   \n",
       "1                         0                         0   \n",
       "2                         0                         0   \n",
       "3                         0                         0   \n",
       "4                         0                         0   \n",
       "\n",
       "   stalk-color-below-ring_n  stalk-color-below-ring_o  \\\n",
       "0                         0                         0   \n",
       "1                         0                         0   \n",
       "2                         0                         0   \n",
       "3                         0                         0   \n",
       "4                         0                         0   \n",
       "\n",
       "   stalk-color-below-ring_p  stalk-color-below-ring_w  \\\n",
       "0                         0                         1   \n",
       "1                         0                         1   \n",
       "2                         0                         1   \n",
       "3                         0                         1   \n",
       "4                         0                         1   \n",
       "\n",
       "   stalk-color-below-ring_y  veil-type_p  veil-color_n  veil-color_o  \\\n",
       "0                         0            1             0             0   \n",
       "1                         0            1             0             0   \n",
       "2                         0            1             0             0   \n",
       "3                         0            1             0             0   \n",
       "4                         0            1             0             0   \n",
       "\n",
       "   veil-color_w  veil-color_y  ring-number_n  ring-number_o  ring-number_t  \\\n",
       "0             1             0              0              1              0   \n",
       "1             1             0              0              1              0   \n",
       "2             1             0              0              1              0   \n",
       "3             1             0              0              1              0   \n",
       "4             1             0              0              1              0   \n",
       "\n",
       "   ring-type_e  ring-type_f  ring-type_l  ring-type_n  ring-type_p  \\\n",
       "0            0            0            0            0            1   \n",
       "1            0            0            0            0            1   \n",
       "2            0            0            0            0            1   \n",
       "3            0            0            0            0            1   \n",
       "4            1            0            0            0            0   \n",
       "\n",
       "   spore-print-color_b  spore-print-color_h  spore-print-color_k  \\\n",
       "0                    0                    0                    1   \n",
       "1                    0                    0                    0   \n",
       "2                    0                    0                    0   \n",
       "3                    0                    0                    1   \n",
       "4                    0                    0                    0   \n",
       "\n",
       "   spore-print-color_n  spore-print-color_o  spore-print-color_r  \\\n",
       "0                    0                    0                    0   \n",
       "1                    1                    0                    0   \n",
       "2                    1                    0                    0   \n",
       "3                    0                    0                    0   \n",
       "4                    1                    0                    0   \n",
       "\n",
       "   spore-print-color_u  spore-print-color_w  spore-print-color_y  \\\n",
       "0                    0                    0                    0   \n",
       "1                    0                    0                    0   \n",
       "2                    0                    0                    0   \n",
       "3                    0                    0                    0   \n",
       "4                    0                    0                    0   \n",
       "\n",
       "   population_a  population_c  population_n  population_s  population_v  \\\n",
       "0             0             0             0             1             0   \n",
       "1             0             0             1             0             0   \n",
       "2             0             0             1             0             0   \n",
       "3             0             0             0             1             0   \n",
       "4             1             0             0             0             0   \n",
       "\n",
       "   population_y  habitat_d  habitat_g  habitat_l  habitat_m  habitat_p  \\\n",
       "0             0          0          0          0          0          0   \n",
       "1             0          0          1          0          0          0   \n",
       "2             0          0          0          0          1          0   \n",
       "3             0          0          0          0          0          0   \n",
       "4             0          0          1          0          0          0   \n",
       "\n",
       "   habitat_u  habitat_w  Y  \n",
       "0          1          0  1  \n",
       "1          0          0  0  \n",
       "2          0          0  0  \n",
       "3          1          0  1  \n",
       "4          0          0  0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Numeric_df = pd.DataFrame(X)\n",
    "Numeric_df['Y'] = Y\n",
    "Numeric_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bruises_f                     0.501530\n",
       "bruises_t                     0.501530\n",
       "gill-color_b                  0.538808\n",
       "gill-size_b                   0.540024\n",
       "gill-size_n                   0.540024\n",
       "ring-type_p                   0.540469\n",
       "stalk-surface-below-ring_k    0.573524\n",
       "stalk-surface-above-ring_k    0.587658\n",
       "odor_f                        0.623842\n",
       "odor_n                        0.785557\n",
       "Y                             1.000000\n",
       "Name: Y, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(num=None, figsize=(12, 10), dpi=80, facecolor='w', edgecolor='k')\n",
    "\n",
    "corr= Numeric_df.corr()\n",
    "sns.heatmap(corr, xticklabels=corr.columns.values, yticklabels=corr.columns.values)\n",
    "\n",
    "# Selecting only correlated features\n",
    "corr_y = abs(corr[\"Y\"])\n",
    "highest_corr = corr_y[corr_y >0.5]\n",
    "highest_corr.sort_values(ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_Reduced2 = X[['bruises_f' , 'bruises_t' , 'gill-color_b' , 'gill-size_b' , 'gill-size_n' , 'ring-type_p' , 'stalk-surface-below-ring_k' , 'stalk-surface-above-ring_k' , \n",
    "                'odor_f', 'odor_n']]\n",
    "X_Reduced2 = StandardScaler().fit_transform(X_Reduced2)\n",
    "X_Train3, X_Test3, Y_Train3, Y_Test3 = train_test_split(X_Reduced2, Y, test_size = 0.30, random_state = 101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.06655320300001222\n",
      "[[1248   26]\n",
      " [  46 1118]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.98      0.97      1274\n",
      "           1       0.98      0.96      0.97      1164\n",
      "\n",
      "    accuracy                           0.97      2438\n",
      "   macro avg       0.97      0.97      0.97      2438\n",
      "weighted avg       0.97      0.97      0.97      2438\n",
      "\n"
     ]
    }
   ],
   "source": [
    "start = time.process_time()\n",
    "trainedsvm = svm.LinearSVC().fit(X_Train3, Y_Train3)\n",
    "print(time.process_time() - start)\n",
    "predictionsvm = trainedsvm.predict(X_Test3)\n",
    "print(confusion_matrix(Y_Test3,predictionsvm))\n",
    "print(classification_report(Y_Test3,predictionsvm))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Univariate Feature Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Univariate Feature Selection is a statistical method used to select the features which have the strongest relationship with our corrispondent labels. Using the **SelectKBest** method we can decide which metrics to use to evaluate our features and the number of K best features we want to keep. Different types of scoring functions are available depending on our needs: \n",
    "- Classification: chi2, f_classif, mutual_info_classif\n",
    "- Regression: f_regression, mutual_info_regression\n",
    "\n",
    "In this example, we will be using chi2. [Chi-squared (Chi2)](https://en.wikipedia.org/wiki/Chi-squared_test) can take as input just non-negative values, therefore, first of all we scale our input data in a range between 0 and 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_max_scaler = preprocessing.MinMaxScaler()\n",
    "Scaled_X = min_max_scaler.fit_transform(X2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.1043402509999964\n",
      "[[1015  259]\n",
      " [  41 1123]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.80      0.87      1274\n",
      "           1       0.81      0.96      0.88      1164\n",
      "\n",
      "    accuracy                           0.88      2438\n",
      "   macro avg       0.89      0.88      0.88      2438\n",
      "weighted avg       0.89      0.88      0.88      2438\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import chi2\n",
    "\n",
    "X_new = SelectKBest(chi2, k=2).fit_transform(Scaled_X, Y)\n",
    "X_Train3, X_Test3, Y_Train3, Y_Test3 = train_test_split(X_new, Y, test_size = 0.30, random_state = 101)\n",
    "start = time.process_time()\n",
    "trainedforest = RandomForestClassifier(n_estimators=700).fit(X_Train3,Y_Train3)\n",
    "print(time.process_time() - start)\n",
    "predictionforest = trainedforest.predict(X_Test3)\n",
    "print(confusion_matrix(Y_Test3,predictionforest))\n",
    "print(classification_report(Y_Test3,predictionforest))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Lasso Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When applying regularization to a Machine Learning model, we add a penalty to the model parameters so that to avoid that our model tries to resemble too closely our input data. In this way, we can make our model less complex and we can avoid overfitting (making learn to our model not just the key data characheteristics but also it's intrinsic noise). <br>\n",
    "\n",
    "One of the possible Regularization Methods is Lasso (L1) Regrssion. When using Lasso Regression, the coefficients of the inputs features gets shrinken if they are not positively contributing towards our Machine Learning model training. In this way, some of the features might get automatically discarded assigning them coefficients equal to zero.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LassoCV Best Alpha Scored:  0.00039648980844788386\n",
      "LassoCV Model Accuracy:  0.9971840741918596\n",
      "Variables Eliminated:  73\n",
      "Variables Kept:  44\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.3780035058582667, tolerance: 0.14200436158986993\n",
      "  positive)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LassoCV\n",
    "\n",
    "regr = LassoCV(cv=5, random_state=101)\n",
    "regr.fit(X_Train,Y_Train)\n",
    "print(\"LassoCV Best Alpha Scored: \", regr.alpha_)\n",
    "print(\"LassoCV Model Accuracy: \", regr.score(X_Test, Y_Test))\n",
    "model_coef = pd.Series(regr.coef_, index = list(X.columns[:-1]))\n",
    "print(\"Variables Eliminated: \", str(sum(model_coef == 0)))\n",
    "print(\"Variables Kept: \", str(sum(model_coef != 0))) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Most Important Features Identified using Lasso (!0)')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(num=None, figsize=(12, 10), dpi=80, facecolor='w', edgecolor='k')\n",
    "\n",
    "top_coef = model_coef.sort_values()\n",
    "top_coef[top_coef != 0].plot(kind = \"barh\")\n",
    "plt.title(\"Most Important Features Identified using Lasso (!0)\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
